{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spline features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import SplineTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's simulate the a sine relation between a\n",
    "# predictor variable X and y\n",
    "\n",
    "X = np.linspace(-1, 11, 20)\n",
    "y = np.sin(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.dpi\"] = 450"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'X')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "\n",
    "plt.plot(X, y)\n",
    "plt.ylabel(\"y\")\n",
    "plt.xlabel(\"X\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's fit a linear model\n",
    "\n",
    "linmod = Ridge(random_state=10)\n",
    "linmod.fit(X.reshape(-1, 1), y)\n",
    "pred = linmod.predict(X.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bdbabd6590>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the preditions\n",
    "\n",
    "plt.plot(X, y)\n",
    "plt.plot(X, pred)\n",
    "plt.ylabel(\"y\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.legend([\"y\", \"linear\"], bbox_to_anchor=(1, 1), loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var_sp_0</th>\n",
       "      <th>var_sp_1</th>\n",
       "      <th>var_sp_2</th>\n",
       "      <th>var_sp_3</th>\n",
       "      <th>var_sp_4</th>\n",
       "      <th>var_sp_5</th>\n",
       "      <th>var_sp_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.082009</td>\n",
       "      <td>0.627011</td>\n",
       "      <td>0.289425</td>\n",
       "      <td>0.001555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.032342</td>\n",
       "      <td>0.526705</td>\n",
       "      <td>0.428512</td>\n",
       "      <td>0.012441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.008335</td>\n",
       "      <td>0.393741</td>\n",
       "      <td>0.555936</td>\n",
       "      <td>0.041989</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000656</td>\n",
       "      <td>0.256111</td>\n",
       "      <td>0.643704</td>\n",
       "      <td>0.099529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   var_sp_0  var_sp_1  var_sp_2  var_sp_3  var_sp_4  var_sp_5  var_sp_6\n",
       "0  0.166667  0.666667  0.166667  0.000000       0.0       0.0       0.0\n",
       "1  0.082009  0.627011  0.289425  0.001555       0.0       0.0       0.0\n",
       "2  0.032342  0.526705  0.428512  0.012441       0.0       0.0       0.0\n",
       "3  0.008335  0.393741  0.555936  0.041989       0.0       0.0       0.0\n",
       "4  0.000656  0.256111  0.643704  0.099529       0.0       0.0       0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's obtain create spline variables from X\n",
    "\n",
    "spl = SplineTransformer(degree=3, n_knots=5)\n",
    "\n",
    "X_t = spl.fit_transform(X.reshape(-1, 1))\n",
    "\n",
    "X_df = pd.DataFrame(X_t, columns=spl.get_feature_names_out([\"var\"]))\n",
    "\n",
    "X_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLot the spline representation of X\n",
    "\n",
    "plt.plot(X, X_t)\n",
    "plt.legend(spl.get_feature_names_out([\"var\"]), bbox_to_anchor=(1, 1), loc=\"upper left\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Splines values\")\n",
    "plt.title(\"Splines\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "linmod = Ridge(random_state=10)\n",
    "linmod.fit(X_t, y)\n",
    "pred = linmod.predict(X_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bdbfcf8b80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, y)\n",
    "plt.plot(X, pred)\n",
    "plt.ylabel(\"y\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.legend([\"y\", \"splines\"], bbox_to_anchor=(1, 1), loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j6+99MJB4m/Xer8TQ2/ugWTHUc9qLncDCiPilpL2ANZJWAfMpuvEXS1pE0Y3/WV7bjT+Loht/VqkbfyZFYrdG0or0C9LMrO1FREiKBm5vCbAEYObMmdHZ2Vl1va6uLnpb1m7qjXX+AE6HUK+F03dy7j2v/3pcf1JnzdtoZrw94+jrtW3l69abcrz9vQ8GEu9A/h8DUYmht/dBs2IY9JBlRGyq9HBFxDPA/RS/9tyNb2YjwZbUhpH+bk3lG4HJpfUmpbLeys3MGnNiWElTgPcCt5ChGz9Xt/3C6Ttr6tJshsHU26jXKNfrPdLqzVn3UBoKy2gFMA9YnP5eUyo/TdJyitGApyJik6QbgL8vTeQ/HDi9xTGbWZuqOyGTtCfwY+AzEfF0MVWs0Kpu/Fzd9vMXXVdTl2YzDKbeRnWt5nq9R1q9OeseSkNhrSDpcqATGC9pA8U0i8XAFZJOAR4Fjk+rr6SYK9tNMV/2YwARsU3S2cBtab0vRUTPAwXMbISqK5OQtAtFMnZpRFyVirdImpB+Edbajd/Zo7yrnrjMzBopIk7sZdGhVdYN4NRetrMUWNrA0GwE6nl5oYXTd7bFXDGrz6DnkKWjJi8C7o+Ir5UWVbrx4fXd+HPTSRMPJnXjAzcAh0sal7ryD09lZmZmZiNCPT1k7wc+Ctwj6c5UdgZDvBt/IBc2NTMzM2uEQSdkEfELQL0sdje+mZmZWY1aPxvdshlI79/6xUc3MRIzMzMr86WTzMzMzDJzQmZmZmaWmYcszczMauCDvqyZ3ENmZmZmlpkTMjMzM7PMnJCZmZmZZeaEzMzMzCwzJ2RmZmZmmTkhMzMzM8vMCZmZmZlZZk7IzMzMzDJzQmZmZmaWmc/Ub2ZmDdPb2ewXTt/J/B7L1i8+uhUhmQ0J7iEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZmWXmhMzMzMwsMydkZmZmZpn5xLBmZiNQbydwrcYncLV6lN9r1U4QbAX3kJmZmZll5oTMzMzMLDMPWVpVfQ1n9Oxy9nCGmZlZfdxDZmZmZpaZe8jMzOogaT3wDPASsDMiZkraB/gRMAVYDxwfEdslCfgGcBTwHDA/In6ZI+52MJADC8x6Gm4HptTVQyZpqaStktaWyvaRtErSuvR3XCqXpPMldUu6W9IBpefMS+uvkzSvnpjMzDL444iYEREz0+NFwE0RMRW4KT0GOBKYmm4LgAtaHqmZtaV6e8guBr4FXFIqqzREiyUtSo8/y2sbolkUDdGs9EvyTGAmEMAaSSsiYnudsZmZ5TIH6Ez3lwFdFO3gHOCSiAhgtaSxkiZExKYsUZqNEEOhN7auhCwifi5pSo/iATVEad1VEbENQNIqYDZweT2xmZm1SAA3SgrgexGxBOgoJVmbgY50fyLwWOm5G1LZaxIySQsoetDo6Oigq6urasU7duzodVl/Fk7fWfO6A6mjt+12jBlYnTkNpVhhaMU73GId7OevmmbMIRtoQ9Rb+ev01kjV0yj1NNA3Sq43V843dc+6G/mG7Esj/89Dod6cdefc5yHoAxGxUdKbgVWSHigvjIhIyVrNUlK3BGDmzJnR2dlZdb2uri56W9afgZycc/1JtdfR23YXTt/JufcMjWnLQylWGFrxDrdYB/LZ6E9TX5XBNET9bK9qI1VPo9TTQM8gnOvNlfNN3bPuRr4h+9LI//NQqDdn3Tn3eaiJiI3p71ZJVwMHAVsqQ5FpJGBrWn0jMLn09EmpzMxGuGac9mJLaoCosSFyA2VmQ5KkPSTtVbkPHA6sBVYAlQOU5gHXpPsrgLnpIKeDgac8f8zMoDkJ2UAbohuAwyWNS0dkHp7KzMzaXQfwC0l3AbcC10XET4DFwGGS1gEfSo8BVgIPA93AhcCnWh+ymbWjusa8JF1OMSl/vKQNFEdLLgaukHQK8ChwfFp9JcW5d7opzr/zMYCI2CbpbOC2tN6XKhP8zczaWUQ8DLynSvkTwKFVygM4tQWhmdkQU+9Rlif2smhADVFELAWW1hOLmZmZ2VDlSyeZmZmZZeaEzMzMzCyzoXEyEDMzy2YonOXcbKhzD5mZmZlZZk7IzMzMzDJzQmZmZmaWmeeQWd0GMr9k/eKjmxiJmZnZ0OQeMjMzM7PMnJCZmZmZZeaEzMzMzCwzJ2RmZmZmmTkhMzMzM8vMCZmZmZlZZk7IzMzMzDJzQmZmZmaWmRMyMzMzs8yckJmZmZll5ksnWUv5MktmZmavNyISsoEkAWZmZmat5iFLMzMzs8xGRA+ZDU09ezYXTt/J/F56Oz28aWZmQ5l7yMzMzMwycw+Zmdkw4fmyZkOXe8jMzMzMMnNCZmZmZpaZEzIzMzOzzDyHzIYFn3DWzMyGMidkZn2Ysui6Pk+3UeZEz8zMBqtthiwlzZb0oKRuSYtyx2Nm1ixu78ysp7boIZM0Cvg2cBiwAbhN0oqIuC9vZDYcNevUAMN52HQ471urub0zs2raIiEDDgK6I+JhAEnLgTmAGygblmpNcGodLh0MJ07ZuL0zs9dRROSOAUnHAbMj4uPp8UeBWRFxWo/1FgAL0sN3Ag+m++OBx1sUbk+56vY+D/96c9bdqnrfGhH7tqCetlFLe9dHW9dTzvfmQDnW5hlK8Y7kWPts79qlh6wmEbEEWNKzXNLtETEzQ0jZ6vY+D/96c9adc5+t97aup6H0f3KszTOU4nWsvWuXSf0bgcmlx5NSmZnZcOP2zsxep10SstuAqZL2k7QrcAKwInNMZmbN4PbOzF6nLYYsI2KnpNOAG4BRwNKIuHcAm+i3a7+JctXtfR7+9easO+c+D2sNaO/KhtL/ybE2z1CK17H2oi0m9ZuZmZmNZO0yZGlmZmY2YjkhMzMzM8tsyCdkOS5BImmypJsl3SfpXkmfbkW9pfpHSbpD0rUtrnespCslPSDpfknva1G9f5Ve57WSLpf0hibWtVTSVklrS2X7SFolaV36O66Fdf9jer3vlnS1pLGtqLe0bKGkkDS+0fXa4LX7pZdyfo4Gqrf2vB3jlfQGSbdKuivF+sVUvp+kW9L74UfpYJG20PP7qs1jXS/pHkl3Sro9lbXsfTCkEzK9egmSI4FpwImSprWg6p3AwoiYBhwMnNqieis+DdzfwvoqvgH8JCL+AHhPK2KQNBH4S2BmROxPMQn6hCZWeTEwu0fZIuCmiJgK3JQet6ruVcD+EfFu4FfA6S2qF0mTgcOBXzehThukjO3eQFxMvs/RQPXWnrdjvM8Dh0TEe4AZwGxJBwNfAc6LiLcD24FT8oX4Oj2/r9o5VoA/jogZpfOPtex9MKQTMkqXIImIF4DKJUiaKiI2RcQv0/1nKN5sE5tdL4CkScDRwPdbUV+p3jcCHwQuAoiIFyLiyRZVPxoYI2k0sDvwn82qKCJ+DmzrUTwHWJbuLwOObVXdEXFjROxMD1dTnLOq6fUm5wF/B/jIn/aSpd0biJyfo4Hqoz1vu3ijsCM93CXdAjgEuDKVt0Ws8PrvK0miTWPtQ8veB0M9IZsIPFZ6vIEWJUYVkqYA7wVuaVGVX6f4kny5RfVV7Af8BvhB6n7+vqQ9ml1pRGwEvkrRS7MJeCoibmx2vT10RMSmdH8z0NHi+itOBq5vRUWS5gAbI+KuVtRnA5K93Rukdvkc9apHe96W8aYhwDuBrRQ96A8BT5Z+uLXT++HrvPb76k20b6xQJLc3Slqj4vJl0ML3wVBPyLKStCfwY+AzEfF0C+r7MLA1ItY0u64qRgMHABdExHuBZ2lBF34ar59DkRC+BdhD0v9qdr29ieI8MS3vMZL0OYqhlUtbUNfuwBnAF5pdl41MuT5HfemrPW+neCPipYiYQdFbfhDwB3kjqi7z99VgfSAiDqCYDnCqpA+WFzb7fTDUE7JslyCRtAvFh/fSiLiqFXUC7weOkbSeYpjiEEn/1KK6NwAbIqLSE3glRYLWbB8CHomI30TEi8BVwH9rQb1lWyRNAEh/t7aycknzgQ8DJ0VrThz4+xQJ8F3pvTYJ+KWk321B3da/oXrppayfo7700p63bbwAacrIzcD7gLFpSge0z/vhdd9XFPOQ2zFW4JURGSJiK3A1RcLbsvfBUE/IslyCJI2DXwTcHxFfa3Z9FRFxekRMiogpFPv6s4hoSW9RRGwGHpP0zlR0KHBfC6r+NXCwpN3T634orT+gYQUwL92fB1zTqoolzabo8j8mIp5rRZ0RcU9EvDkipqT32gbggPQesPyG6qWXsn2O+tJHe9528Urat3KktaQxwGEU7eHNwHFptbaItZfvq5Now1gBJO0haa/KfYoDmtbSyvdBRAzpG3AUxdFnDwGfa1GdH6DotrwbuDPdjmrxfncC17a4zhnA7Wm//xkY16J6vwg8kD4cPwR2a2Jdl1PMVXuRIhE5hWLew03AOuCnwD4trLubYr5Q5X323VbU22P5emB8K99rvvX7P2t5uzfA+LJ9jgYRa9X2vB3jBd4N3JFiXQt8IZW/Dbg1tRf/p5lt5CDjfuX7ql1jTXHdlW73Vj5XrXwf+NJJZmZmZpkN9SFLMzMzsyHPCZmZmZlZZk7IzMzMzDJzQmZmZmaWmRMyMzMzs8yckJmZmZll5oTMzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZtTlJIentueOw5nFCZjWT1CVpu6TdGrCt9ZJ+K2mHpM2SLpa0ZyPiNDMbjCa2cdslXSdpciPitOHJCZnVRNIU4P8BAjimQZv9k4jYE5gBvBc4vUHbNTMbkCa3cROALcA3G7Td15A0qhnbtdZyQma1mgusBi4G5knaTdKTkvavrCBp3/SL8M3p8Ycl3ZnW+3dJ76624YjYDNxAkZhVtnWMpHvTc7sk/WFp2R+msifTOseUll0s6TuSrk+/TP+vpN+V9PX0K/UBSe8trf9ZSRslPSPpQUmHNuwVM7OhpJlt3H8BVwLTStvaTdJXJf1a0hZJ35U0prT8byVtkvSfkk4uby+1cxdIWinpWeCPU4/c30q6W9Kzki6S1JHawmck/VTSuPT8N0j6J0lPpNhvk9TRsFfSBsUJmdVqLnBpuh0BjAWuAk4srXM88K8RsTUlPUuBTwJvAr4HrKg2FCBpEnAk0J0evwO4HPgMsC+wEvgXSbtK2gX4F+BG4M3AXwCXSnpnjzg+D4wHngf+A/hlenwl8LVUzzuB04A/ioi90n6tH+TrY2ZDWzPbuN2BP6VI+CoWA++g+CH6dmAi8IW0/mzgb4DDgKnAh6rE+2fAOcBewC9S2f+bnvMO4E+A64EzKNrR3wH+Mq03D3gjMDnF/ufAb/t6caz5nJBZvyR9AHgrcEVErAEeomgMLgNOKK1aKQNYAHwvIm6JiJciYhlFcnRwaf1/lvQM8BiwFTgzlf8pcF1ErIqIF4GvAmOA/5aevyewOCJeiIifAdfy2kbz6ohYk36VXg38V0RcEhEvAT+iGB4FeAnYDZgmaZeIWB8RD9XzWpnZ0NPkNu5J4CmKROkfU31Kz/+riNgWEc8Af1+q63jgBxGxNiKeBc6qEvY1EfF/I+Ll1NYBfDMitkTERuDfgFsi4o5SW1hp+16kSMTenmJfExFPD+xVs0ZzQma1mAfcGBGPp8eXpbKbgd0lzUrzL2ZQfOihaNwWpu7wJ1OjNBl4S2m7x6aeqU7gDyh6sEjrPFpZKSJepkjaJqZlj6WyikfTsootpfu/rfJ4z7TdbopeuLOArZKWSyrHZ2YjQzPbuLHAGyh64/9V0u9S9FjtDqwpPfcnqZy0jcdK23mU13usSllNbR/wQ4ppIsvTkOg/pNEHy2h07gCsvaU5DccDoyRtTsW7UXTn7w9cQdE7tQW4Nv3Sg6KxOCcizumvjoj4V0kXU/SEHQv8JzC9FIMoGrqNFL1akyX9Tikp+z3gV4PZv4i4DLhM0t4UQw5fAT46mG2Z2dDTojbuJeAqSd8DPkAxFPpb4F2pN6unTRRtXsXvVdtsf/X2Ec+LwBeBL6ZEcyXwIHDRYLdp9XMPmfXnWIokaBrFr8MZwB9SdIfPpfgl+afASbzalQ9wIfDn6ZelJO0h6WhJe/VSz9eBwyS9h6IBPFrSoelX20KKoYB/B24BngP+TtIukjop5kosH+iOSXqnpEPSnI//omggX+7naWY2vBxLk9u4tHwOMA64P/2YvBA4r3SAwERJR6SnXAHMlzQtzT87s+c26yHpjyVNV3F05tMUQ5hu+zJzQmb9mUcxl+HXEbG5cgO+RdFArQGepehiv77ypIi4HfhEWm87xYT9+b1VEhG/AS4BvhARDwL/i+IQ8ccpEq4/SXPGXkiPj0zLvgPMjYgHBrFvu1FMrH0c2ExxkIBPvWE2sjSzjfsXSTsokp5zgHkRcW9a9tn0nNWSngZ+Crwzbft6ih+pP0vr/KzB+/y7FAc4PQ3cD/wrxTCmZaSIQfd6mpmZmVkDuIfMzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmTsjMzMzMMhuyJ4YdP358TJkypd/1nn32WfbYY4/mB+S626Lu3PW77oFZs2bN4xGxb/9rjly1tHW5P3PtEEPu+h1D+8SQu/7eYui3vYuIIXk78MADoxY333xzTes1g+seefW77oEBbo82aE/a+VZLW5f7M9cOMeSu3zG0Twy56+8thv7au36HLCVNlnSzpPsk3Svp06l8H0mrJK1Lf8elckk6X1K3pLslHVDa1ry0/jpJ80rlB0q6Jz3n/HSpHDMzM7MRoZY5ZDuBhRExjeIq9qdKmgYsAm6KiKnATekxFGdQn5puC4ALoEjgKC7/MAs4CDizksSldT5Ret7s+nfNzMzMbGjoNyGLiE0R8ct0/xmKyyxMBOYAy9JqyyiuB0YqvyT10K0GxkqaABwBrIqIbRGxHVgFzE7L9o6I1alL75LStszMzMyGvQFN6k9XhX8vxQWeOyJiU1q0GehI9ycCj5WetiGV9VW+oUp5tfoXUPS60dHRQVdXV78x79ixo6b1msF15zFS932k1m1mNhzUnJBJ2hP4MfCZiHi6PM0rIkJS0y+KGRFLgCUAM2fOjM7Ozn6f09XVRS3rNYPrzmOk7vtIrdvMbDioKSGTtAtFMnZpRFyVirdImhARm9Kw49ZUvhGYXHr6pFS2EejsUd6VyidVWb9hpiy6ruZ11y8+upFVm9kQImkyxbSJDiCAJRHxjTQH9kfAFGA9cHxEbE8HIH0DOAp4DphfmeKRDlz6fNr0lyNiWSo/ELgYGAOsBD6dpmvUzW2d2dBVy1GWAi4C7o+Ir5UWrQAqR0rOA64plc9NR1seDDyVhjZvAA6XNC5N5j8cuCEte1rSwamuuaVtmZm1kg9iMrMsajnK8v3AR4FDJN2ZbkcBi4HDJK0DPpQeQ/GL72GgG7gQ+BRARGwDzgZuS7cvpTLSOt9Pz3kIuL4B+2ZmNiA+iMnMcul3yDIifgH0dl6wQ6usH8CpvWxrKbC0SvntwP79xWJm1io5D2Ia6AFMlYMqFk7fWcOeFRp9EEbuAzty1+8Y2ieG3PUPNoYhe+kkM7NmyX0Q00APYKocVDF/IHPITup7mwOV+8CO3PU7hvaJIXf9g43BFxc3Myvp6yCmtLzWg5h6K2/qQUxmNjQ5ITMzS3wQk5nl4iFLM7NXVQ5iukfSnansDIqDlq6QdArwKHB8WraS4pQX3RSnvfgYFAcxSaocxASvP4jpYorTXlyPD2IyM5yQmZm9wgcxmVkuHrI0MzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZmWXmhMzMzMwsMydkZmZmZpk5ITMzMzPLzAmZmZmZWWZOyMzMzMwyc0JmZmZmlpkTMjMzM7PMnJCZmZmZZeaEzMzMzCwzJ2RmZmZmmTkhMzMzM8vMCZmZmZlZZk7IzMzMzDJzQmZmZmaWmRMyMzMzs8yckJmZmZll5oTMzMzMLLN+EzJJSyVtlbS2VHaWpI2S7ky3o0rLTpfULelBSUeUymensm5Ji0rl+0m6JZX/SNKujdxBMzMzs3ZXSw/ZxcDsKuXnRcSMdFsJIGkacALwrvSc70gaJWkU8G3gSGAacGJaF+AraVtvB7YDp9SzQ2ZmZmZDTb8JWUT8HNhW4/bmAMsj4vmIeAToBg5Kt+6IeDgiXgCWA3MkCTgEuDI9fxlw7MB2wczMzGxoG13Hc0+TNBe4HVgYEduBicDq0jobUhnAYz3KZwFvAp6MiJ1V1n8dSQuABQAdHR10dXX1G+SOHTtYOP2lWvYHoKZt1mrHjh0N3Z7rbv/6XffQJmkp8GFga0Tsn8rOAj4B/CatdkZpVOB0il79l4C/jIgbUvls4BvAKOD7EbE4le9H8YP0TcAa4KPpR6qZjXCDTcguAM4GIv09Fzi5UUH1JiKWAEsAZs6cGZ2dnf0+p6uri3N/8WzNdaw/qf9t1qqrq4taYmyGkVp37vpd95B3MfAt4JIe5edFxFfLBT2maLwF+Kmkd6TF3wYOo/iReZukFRFxH69O0Vgu6bsUydwFzdoZMxs6BnWUZURsiYiXIuJl4EKKIUmAjcDk0qqTUllv5U8AYyWN7lFuZtZynqJhZrkMqodM0oSI2JQefgSoHIG5ArhM0tcofjFOBW4FBExN3fUbKX5V/llEhKSbgeMoGq15wDWD3RkzsyZp6RSNgU7PqAwZL5y+s8/1yho9xJx72Dp3/Y6hfWLIXf9gY+g3IZN0OdAJjJe0ATgT6JQ0g2LIcj3wSYCIuFfSFcB9wE7g1Ih4KW3nNOAGijkVSyPi3lTFZ4Hlkr4M3AFcNKA9MDNrrpZP0Rjo9IzKkPH8RdfVXEcjp2eUY8gld/2OoX1iyF3/YGPoNyGLiBOrFPeaNEXEOcA5VcpXAiurlD/Mq0OeZmZtJSK2VO5LuhC4Nj3sbSoGvZS/MkUj9ZJ5ioaZvcJn6jcz64OkCaWHPadonCBptzQdozJF4zbSFI10ousTgBUREUBligZ4ioaZldRz2gszs2HFUzTMLBcnZGZmiadomFkuHrI0MzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZmWXmhMzMzMwsMydkZmZmZpk5ITMzMzPLzAmZmZmZWWZOyMzMzMwyc0JmZmZmlpkTMjMzM7PMnJCZmZmZZeaEzMzMzCwzJ2RmZmZmmTkhMzMzM8vMCZmZmZlZZk7IzMzMzDJzQmZmZmaWmRMyMzMzs8yckJmZmZll5oTMzMzMLDMnZGZmZmaZ1ZSQSVoqaauktaWyfSStkrQu/R2XyiXpfEndku6WdEDpOfPS+uskzSuVHyjpnvSc8yWpkTtpZmZm1s5q7SG7GJjdo2wRcFNETAVuSo8BjgSmptsC4AIoEjjgTGAWcBBwZiWJS+t8ovS8nnWZmZmZDVs1JWQR8XNgW4/iOcCydH8ZcGyp/JIorAbGSpoAHAGsiohtEbEdWAXMTsv2jojVERHAJaVtmZm1jEcDzCyX0XU8tyMiNqX7m4GOdH8i8FhpvQ2prK/yDVXKX0fSAopeNzo6Oujq6uo3yB07drBw+kv9rldRyzZrtWPHjoZuz3W3f/2ue8i7GPgWxQ/DispowGJJi9Ljz/La0YBZFD39s0qjATOBANZIWpF+iFZGA24BVlKMBlzfgv0yszZXT0L2iogISdGIbfVTzxJgCcDMmTOjs7Oz3+d0dXVx7i+erbmO9Sf1v81adXV1UUuMzTBS685dv+se2iLi55Km9CieA3Sm+8uALoqE7JXRAGC1pMpoQCdpNABAUmU0oIs0GpDKK6MBTsjMrK6EbIukCRGxKTVCW1P5RmByab1JqWwjrzZqlfKuVD6pyvpmZu2g7UcDKj2UC6fvrGF3Co3u0czdS5q7fsfQPjHkrn+wMdSTkK0A5gGL099rSuWnSVpO0Y3/VErabgD+vjSR/3Dg9IjYJulpSQdTdOPPBb5ZR1xmZk3RrqMBlR7K+Yuuq7mORo4GlGPIJXf9jqF9Yshd/2BjqPW0F5cD/wG8U9IGSadQJGKHSVoHfCg9hmJexMNAN3Ah8CmA1H1/NnBbun2p0qWf1vl+es5DuAvfzNrHljQKwABGA3or92iAmVVVUw9ZRJzYy6JDq6wbwKm9bGcpsLRK+e3A/rXEYmbWYh4NMLOma8ikfjOz4SCNBnQC4yVtoDhacjFwRRoZeBQ4Pq2+EjiKomf/OeBjUIwGSKqMBsDrRwMuBsZQjAR4NMDMACdkZmav8GiAmeXia1mamZmZZeaEzMzMzCwzJ2RmZmZmmTkhMzMzM8vMCZmZmZlZZk7IzMzMzDJzQmZmZmaWmRMyMzMzs8yckJmZmZll5oTMzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZmWXmhMzMzMwsMydkZmZmZpk5ITMzMzPLzAmZmZmZWWZOyMzMzMwyc0JmZmZmlpkTMjMzM7PMnJCZmZmZZVZ3QiZpvaR7JN0p6fZUto+kVZLWpb/jUrkknS+pW9Ldkg4obWdeWn+dpHn1xmVmZmY2VDSqh+yPI2JGRMxMjxcBN0XEVOCm9BjgSGBqui0ALoAigQPOBGYBBwFnVpI4M7N24B+fZtZMzRqynAMsS/eXAceWyi+JwmpgrKQJwBHAqojYFhHbgVXA7CbFZmY2WP7xaWZNMboB2wjgRkkBfC8ilgAdEbEpLd8MdKT7E4HHSs/dkMp6K38NSQsoGjc6Ojro6urqN7gdO3awcPpLNe9MLdus1Y4dOxq6Pdfd/vW77hFnDtCZ7i8DuoDPUvrxCayWVPnx2Un68QkgqfLj8/LWhm1m7aYRCdkHImKjpDcDqyQ9UF4YEZGStbqlZG8JwMyZM6Ozs7Pf53R1dXHuL56tuY71J/W/zVp1dXVRS4zNMFLrzl2/6x7W2vbHZyUhXjh9Z8070+gEOndSnrt+x9A+MeSuf7Ax1J2QRcTG9HerpKspuuG3SJoQEZvSr8KtafWNwOTS0yelso28+iuzUt5Vb2xmZg3Utj8+Kwnx/EXX1VxHI398lmPIJXf9jqF9Yshd/2BjqGsOmaQ9JO1VuQ8cDqwFVgCVyarzgGvS/RXA3DTh9WDgqfTr8gbgcEnj0nyKw1OZmVlbKP/4BF7z4xNgAD8+q5Wb2QhX76T+DuAXku4CbgWui4ifAIuBwyStAz6UHgOsBB4GuoELgU8BpPkUZwO3pduXKnMszMxy849PM2u2uoYsI+Jh4D1Vyp8ADq1SHsCpvWxrKbC0nnjMzJqkA7haEhTt5mUR8RNJtwFXSDoFeBQ4Pq2/EjiK4sfnc8DHoPjxKany4xP849PMkkZM6jczG9b849PMms2XTjIzMzPLzAmZmZmZWWZOyMzMzMwyc0JmZmZmlpkTMjMzM7PMnJCZmZmZZeaEzMzMzCwzJ2RmZmZmmTkhMzMzM8vMCZmZmZlZZk7IzMzMzDJzQmZmZmaWmRMyMzMzs8yckJmZmZll5oTMzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZmWXmhMzMzMwsMydkZmZmZpmNzh1Au5my6Lqa112/+OgmRmJmZmYjhXvIzMzMzDJrm4RM0mxJD0rqlrQodzxmZs3i9s7MemqLhEzSKODbwJHANOBESdPyRmVm1nhu78ysmnaZQ3YQ0B0RDwNIWg7MAe7LGpWZWeO1RXvn+bJm7aVdErKJwGOlxxuAWZliqVl/DdrC6TuZP4BGr5HatW437O1jIF/I/Sn/z/0/7teQa+9qea/kbHPqqX8g79eBtPn+HLxqqCX/ueJVRDRsY4MOQjoOmB0RH0+PPwrMiojTeqy3AFiQHr4TeLCGzY8HHm9guAPhukde/a57YN4aEfs2Oph2Vkt7N4i2Lvdnrh1iyF2/Y2ifGHLX31sMfbZ37dJDthGYXHo8KZW9RkQsAZYMZMOSbo+ImfWFNziuO4+Ruu8jte4hqN/2bqBtXTu8/rljyF2/Y2ifGHLXP9gY2mJSP3AbMFXSfpJ2BU4AVmSOycysGdzemdnrtEUPWUTslHQacAMwClgaEfdmDsvMrOHc3plZNW2RkAFExEpgZRM2PaAhTtc95OvOXb/rtn41ob1rh9c/dwy56wfHUJE7htz1wyBiaItJ/WZmZmYjWbvMITMzMzMbsYZtQtbKS5NImizpZkn3SbpX0qdT+T6SVklal/6Oa2IMoyTdIena9Hg/Sbek/f9RmjzcrLrHSrpS0gOS7pf0vlbtu6S/Sq/5WkmXS3pDM/dd0lJJWyWtLZVV3VcVzk9x3C3pgCbU/Y/pdb9b0tWSxpaWnZ7qflDSEY2uu7RsoaSQND49buh+W99yX4ZJ0npJ90i6U9LtLaqz5s9hi2M4S9LG9FrcKemoJtbfDt87vcXQytfhDZJulXRXiuGLqbwl34F91H+xpEdKr8GMfjcWEcPuRjFR9iHgbcCuwF3AtCbWNwE4IN3fC/gVxSVR/gFYlMoXAV9pYgx/DVwGXJseXwGckO5/F/jfTax7GfDxdH9XYGwr9p3iBJuPAGNK+zy/mfsOfBA4AFhbKqu6r8BRwPWAgIOBW5pQ9+HA6HT/K6W6p6X3/W7AfunzMKqRdafyyRST0x8Fxjdjv33r8//S0raulxjWV/73Layz5s9hi2M4C/ibFr0G7fC901sMrXwdBOyZ7u8C3JLanZZ8B/ZR/8XAcQPZ1nDtIXvl0iQR8QJQuTRJU0TEpoj4Zbr/DHA/RbIwhyJZIf09thn1S5oEHA18Pz0WcAhwZQvqfiNFw3QRQES8EBFP0qJ9pzgwZYyk0cDuwCaauO8R8XNgW4/i3vZ1DnBJFFYDYyVNaGTdEXFjROxMD1dTnNOqUvfyiHg+Ih4Buik+Fw2rOzkP+DugPBm1ofttfWppW9cuBvg5bGUMLZP7e6efGFomtTM70sNd0i1o0XdgH/UP2HBNyKpdmqQlbxJJU4D3UmTJHRGxKS3aDHQ0qdqvU3wpvpwevwl4svRF3cz93w/4DfADFUOm35e0By3Y94jYCHwV+DVFIvYUsIbW7XtFb/va6vfhyRQ9Uy2pW9IcYGNE3NVjUbbP3wjUDq91ADdKWqPiCgO5tKq97c9paah+abOHTSsyfe/0FQO08HVQMWXnTmArsIqi17hl3wM964+IymtwTnoNzpO0W3/bGa4JWRaS9gR+DHwmIp4uL4uiP7Phh7RK+jCwNSLWNHrbNRpN0W1/QUS8F3iWopv8FU3c93EUvwb3A94C7AHMbnQ9A9Gsfe2PpM8BO4FLW1Tf7sAZwBdaUZ+1tQ9ExAHAkcCpkj6YO6Bcn0PgAuD3gRkUPxLPbXaFOb53aoihpa9DRLwUETMoRggOAv6gmfX1V7+k/YHTUxx/BOwDfLa/7QzXhKymSzE1kqRdKN6Ql0bEVal4S2WoJv3d2oSq3w8cI2k9xXDFIcA3KIaJKueZa+b+bwA2lH4RXEmRoLVi3z8EPBIRv4mIF4GrKF6PVu17RW/72pL3oaT5wIeBk1ID3Iq6f58iEb4rvfcmAb+U9LstqNtelf21Tj3VRMRW4GrqGBqvUyvanD5FxJb05fwycCFNfi0yfu/0GUOrX4eKNF3mZuB9tP57oFz/7DScGxHxPPADangNhmtC1tJLk6Q5WxcB90fE10qLVgDz0v15wDWNrjsiTo+ISRExhWI/fxYRJ1G8KY5rZt2p/s3AY5LemYoOBe6jBftOMVR5sKTd0/+gUndL9r2kt31dAcxV4WDgqdJQQkNImk0xXH1MRDzXI6YTJO0maT9gKnBro+qNiHsi4s0RMSW99zZQTO7dTAv2216R9TJMkvaQtFflPsVBJq87ErdFWtHm9KnHXMmP0MTXIuf3Tn8xtPh12Ffp6HJJY4DDKOayteR7oJf6HyglxaKYv9b/azCQIwCG0o3iSK9fUYwlf67JdX2Aolv4buDOdDuKYi7XTcA64KfAPk2Oo5NXj7J8G8UXcDfwf4DdmljvDOD2tP//DIxr1b4DXwQeSG/2H1IcVdi0fQcup+iCf5EiCTmlt32lOPrm2+k9eA8wswl1d1PMIaq8775bWv9zqe4HgSMbXXeP5et59SjLhu63b/3+b1rW1lWp+20UR3beBdzbqvoH8jlscQw/TO/5uykSowlNrD/7904fMbTydXg3cEeqay3whdJ7s+nfgX3U/7P0GqwF/ol0JGZfN5+p38zMzCyz4TpkaWZmZjZkOCEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZmWXmhMzMzMwsMydkZmZmZpk5ITMzMzPLzAmZmZmZWWZOyMzMzMwyc0JmZmZmlpkTMjMzM7PMnJBZ25F0saQv1/H8HZLe1siYzMzMmskJmfVL0npJv02JzpaUMO2ZOy4ASV2SPl4ui4g9I+LhXDGZ2fCQ2pftknZr0PbmS7pH0nOSNku6QNLYRmzbhj4nZFarP4mIPYEDgJnA5zPHY2bWNJKmAP8PEMAxDdjeQuArwN8CbwQOBt4KrJK0a73bt6HPCZkNSERsBK4H9pd0jKR7JT2Zfkn+YWW91Kt2uqT70i/MH0h6Q1o2X9IvytuVFJLe3rM+SeMkXSvpN2k710qalJadQ9Fgfiv13n2r57YkvVHSJen5j0r6vKTfKcch6atp249IOrI5r5yZDTFzgdXAxcA8Sbultm7/ygqS9k2jB29Ojz8s6c603r9Lencq3xv4IvAXEfGTiHgxItYDxwNTgP+V1hsl6QxJD0l6RtIaSZPTsndJWiVpWxqpOCOVv2aKh6ROSRtKj3tti629OCGzAUmNw1HAM8DlwGeAfYGVwL/0+KV3EnAE8PvAOxhcr9rvAD+g+CX5e8BvgW8BRMTngH8DTkvDlKdVef43KX6Nvg347xSN7MdKy2cBDwLjgX8ALpKkQcRpZsPLXODSdDsCGAtcBZxYWud44F8jYquk9wJLgU8CbwK+B6xIw53/DXhDev4rImIHRdt5WCr667T9o4C9gZOB5yTtBfwU+AnwFuDtwE0D2JdGtMXWZE7IrFb/LOlJ4BfAvwL3AddFxKqIeBH4KjCGouGp+FZEPBYR24BzeG1DVpOIeCIifhwRz0XEM2k7/72W50oaBZwAnB4Rz6RfpOcCHy2t9mhEXBgRLwHLgAlAx0DjNLPhQ9IHKH4EXhERa4CHgD8DLqNoUyoqZQALgO9FxC0R8VJELAOepxiaHA88HhE7q1S3KS0H+Djw+Yh4MAp3RcQTwIeBzRFxbkT8V2rPbhnALtXdFlvzjc4dgA0Zx0bETysPJF0APFp5HBEvS3oMmFh6zmOl+49S/LIbEEm7A+cBs4FxqXgvSaNSEtWX8cAu5TjT/XKMmyt3IuK51DnWFgcsmFk284AbI+Lx9PiyVHYgsLukWcAWYAZwdVrnrRRDm39R2s6uFO3e48B4SaOrJGUT0nKAyRTJX0+9ldeq7rbYms8JmQ3WfwLTKw/SMN9kYGNpncml+7+XngPwLLB76bm/20c9C4F3ArMiYrOkGcAdQGVYMfp47uPAixQN5X2lODb2+gwzG9EkjaEYihwlqfKDbTeKIcv9gSsoepi2ANemnnsokp5zIuKcKtt8I0Vv2f9Iz6+U7wkcCZxR2sbvA2t7bOIxXtszV/aa9hSo1p721hZbG/GQpQ3WFcDRkg6VtAtF4vQ88O+ldU6VNEnSPsDngB+l8ruAd0makSaXntVHPXtRzBt7Mm3nzB7Lt1DMD3ud1IN2BXCOpL0kvZVijsY/DWA/zWxkORZ4CZhG0QM2A/hDivmqcyl6y/6UYl7WZaXnXQj8uaRZKuwh6WhJe0XEUxST+r8pabakXdJRnFcAG4Afpm18Hzhb0tS0jXdLehNwLTBB0mfSwQV7pV46gDuBoyTtk37cfqbKPvXWFlsbcUJmgxIRD1IcGfRNip6oP6E4NcYLpdUuA24EHqbobv9yeu6vgC9RTFJdRzEvrTdfp5ib9jjFEU8/6bH8G8Bx6eih86s8/y8ofkE+nOq5jGLirZlZNfOAH0TEryNic+VGcTDRScAaijblLRRHnAMQEbcDn0jrbQe6gfml5f9A0RP2VeBp4BaKnq9DI+L5tNrXKJK0G9M6FwFjUi/cYRTt7GaKdvOP03N+SPEjd316XrVkq2pbbO1FEX2N+JgNjqT1wMfL887MzKy13BYPHe4hMzMzM8vMCZmZmZlZZh6yNDMzM8vMPWRmZmZmmQ3Z85CNHz8+pkyZUtO6zz77LHvssUdzA3LdbVH3SNznoVz3mjVrHo+IfRsc0rAyVNq6atopnnaKBdornnaKBdornkbG0m97FxFD8nbggQdGrW6++eaa12001z0y6nXdgwPcHm3QnrTzbai0ddW0UzztFEtEe8XTTrFEtFc8jYylv/bOQ5ZmZmZmmTkhMzMzM8vMCZmZmZlZZk7IzMzMzDJzQmZmZmaW2ZA97cVA3LPxKeYvuq6mddcvPrrJ0ZiZNYfbOrOhyz1kZmaJpMmSbpZ0n6R7JX06le8jaZWkdenvuFQuSedL6pZ0t6QDStual9ZfJ2leqfxASfek55wvSa3fUzNrN07IzMxetRNYGBHTgIOBUyVNAxYBN0XEVOCm9BjgSGBqui0ALoAigQPOBGYBBwFnVpK4tM4nSs+b3YL9MrM254TMzCyJiE0R8ct0/xngfmAiMAdYllZbBhyb7s8BLknnfVwNjJU0ATgCWBUR2yJiO7AKmJ2W7R0Rq9OJIi8pbcvMRrARMYfMzGygJE0B3gvcAnRExKa0aDPQke5PBB4rPW1DKuurfEOV8p51L6DocaOjo4Ourq6aYu4YAwun76xp3Vq3WY8dO3a0pJ5atFMs0F7xtFMs0F7xtDKWfhMySUuBDwNbI2L/VHYWRZf7b9JqZ0TEyrTsdOAU4CXgLyPihlQ+G/gGMAr4fkQsTuX7AcuBNwFrgI9GxAuN2kEzs4GStCfwY+AzEfF0eZpXRISkaGb9EbEEWAIwc+bM6OzsrOl537z0Gs69p7bf2etPqm2b9ejq6qLW2JutnWKB9oqnnWKB9oqnlbHUMmR5MdXnOJwXETPSrZKMTQNOAN6VnvMdSaMkjQK+TTHfYhpwYloX4CtpW28HtlMkc2ZmWUjahSIZuzQirkrFW9JwI+nv1lS+EZhcevqkVNZX+aQq5WY2wvWbkEXEz4FtNW5vDrA8Ip6PiEeAbooJrQcB3RHxcOr9Wg7MSUcXHQJcmZ5fnpthZtZSqU26CLg/Ir5WWrQCqBwpOQ+4plQ+Nx1teTDwVBravAE4XNK4NJn/cOCGtOxpSQenuuaWtmVmI1g9c8hOkzQXuJ3iqKTtFHMhVpfWKc+P6DmfYhbFMOWTEbGzyvqvMxTnVeQcCx+JdY/EfR7JdTfB+4GPAvdIujOVnQEsBq6QdArwKHB8WrYSOIrix+dzwMcAImKbpLOB29J6X4qIyg/bT1GMPIwBrk83MxvhBpuQXQCcDUT6ey5wcqOC6s1QnFeRcyx8JNY9Evd5JNfdaBHxC6C384IdWmX9AE7tZVtLgaVVym8H9q8jTDMbhgaVkEXElsp9SRcC16aHvc2boJfyJygOEx+desk8n8LMzMxGnEGdh6wyuTX5CLA23V8BnCBpt3T05FTgVopu+6mS9pO0K8XE/xXp1+XNwHHp+eW5GWZmZmYjQi2nvbgc6ATGS9pAcfbpTkkzKIYs1wOfBIiIeyVdAdxHccbrUyPipbSd0ygmuo4ClkbEvamKzwLLJX0ZuINiQq2ZmZnZiNFvQhYRJ1Yp7jVpiohzgHOqlK+kmADbs/xhiqMwzczMzEYkXzrJzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZmWXmhMzMzMwsMydkZmZmZpk5ITMzMzPLzAmZmZmZWWZOyMzMzMwy6zchk7RU0lZJa0tl/yjpAUl3S7pa0thUPkXSbyXdmW7fLT3nQEn3SOqWdL4kpfJ9JK2StC79HdeE/TQzMzNrW7X0kF0MzO5RtgrYPyLeDfwKOL207KGImJFuf14qvwD4BDA13SrbXATcFBFTgZvSYzMzM7MRo9+ELCJ+DmzrUXZjROxMD1cDk/rahqQJwN4RsToiArgEODYtngMsS/eXlcrNzMzMRoTRDdjGycCPSo/3k3QH8DTw+Yj4N2AisKG0zoZUBtAREZvS/c1AR28VSVoALADo6Oigq6urpgA7xsDC6Tv7XxFq3matduzY0fBtuu72q9d156nbzGy4qCshk/Q5YCdwaSraBPxeRDwh6UDgnyW9q9btRURIij6WLwGWAMycOTM6Oztr2u43L72Gc++pbVfXn1TbNmvV1dVFrXE22kiseyTu80iu28xsuBh0QiZpPvBh4NA0DElEPA88n+6vkfQQ8A5gI68d1pyUygC2SJoQEZvS0ObWwcZkZmZmNhQN6rQXkmYDfwccExHPlcr3lTQq3X8bxeT9h9OQ5NOSDk5HV84FrklPWwHMS/fnlcrNzMzMRoR+e8gkXQ50AuMlbQDOpDiqcjdgVTp7xep0ROUHgS9JehF4GfjziKgcEPApiiM2xwDXpxvAYuAKSacAjwLHN2TPzMzMzIaIfhOyiDixSvFFvaz7Y+DHvSy7Hdi/SvkTwKH9xWFmZmY2XPlM/WZmSS8nwj5L0sbSCa+PKi07PZ3s+kFJR5TKZ6eybkmLSuX7Sbollf9I0q6t2zsza2dOyMzMXnUxrz8RNsB5pRNerwSQNA04AXhXes53JI1K82i/DRwJTANOTOsCfCVt6+3AduCUpu6NmQ0ZTsjMzJJqJ8LuwxxgeUQ8HxGPAN3AQenWHREPR8QLwHJgTjqg6RDgyvR8nwjbzF7RiBPDmpkNd6dJmgvcDiyMiO0UJ7deXVqnfMLrx3qUzwLeBDxZuspJef3XGIonwa6mnU4a3E6xQHvF006xQHvF08pYnJCZmfXtAuBsINLfcymuUNI0Q/Ek2NW000mD2ykWaK942ikWaK94WhmLEzIzsz5ExJbKfUkXAtemhxuByaVVyye8rlb+BDBW0ujUS1Ze38xGOM8hMzPrQ7qCSMVHgMoRmCuAEyTtJmk/ihNh3wrcBkxNR1TuSjHxf0W6osnNwHHp+T4Rtpm9wj1kZmZJLyfC7pQ0g2LIcj3wSYCIuFfSFcB9FNf0PTUiXkrbOQ24ARgFLI2Ie1MVnwWWS/oycAe9nNPRzEYeJ2RmZslAToSd1j8HOKdK+UpgZZXyhymOwjQzew0PWZqZmZll5oTMzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmNSVkkpZK2ippbalsH0mrJK1Lf8elckk6X1K3pLslHVB6zry0/jpJ80rlB0q6Jz3n/HTNNzMzM7MRodYesouB2T3KFgE3RcRU4Kb0GOBIihMkTqW4FtsFUCRwFOf0mUVx2PeZlSQurfOJ0vN61mVmZmY2bNWUkEXEz4FtPYrnAMvS/WXAsaXyS6KwmuJSIROAI4BVEbEtXZh3FTA7Lds7IlanM1lfUtqWmZmZ2bBXz4lhOyJiU7q/GehI9ycCj5XW25DK+irfUKX8dSQtoOh1o6Ojo+YrsHeMgYXTd9a0bqOv6p7zqvUjse6RuM8juW4zs+GiIWfqj4iQFI3YVj/1LAGWAMycOTNqvQL7Ny+9hnPvqW1X159U2zZrlfOq9SOx7pG4zyO5bjOz4aKeoyy3VC66m/5uTeUbgcml9Salsr7KJ1UpNzMzMxsR6knIVgCVIyXnAdeUyuemoy0PBp5KQ5s3AIdLGpcm8x8O3JCWPS3p4HR05dzStszMzMyGvZrG8SRdDnQC4yVtoDhacjFwhaRTgEeB49PqK4GjgG7gOeBjABGxTdLZwG1pvS9FROVAgU9RHMk5Brg+3czMzMxGhJoSsog4sZdFh1ZZN4BTe9nOUmBplfLbgf1ricXMzMxsuPGZ+s3MzMwyc0JmZmZmlpkTMjMzM7PMnJCZmZmZZeaEzMzMzCwzJ2RmZmZmmTkhMzMzM8vMCZmZmZlZZk7IzMzMzDJzQmZmZmaWmRMyMzMzs8yckJmZmZll5oTMzCyRtFTSVklrS2X7SFolaV36Oy6VS9L5krol3S3pgNJz5qX110maVyo/UNI96TnnS1Jr99DM2pUTMjOzV10MzO5Rtgi4KSKmAjelxwBHAlPTbQFwARQJHHAmMAs4CDizksSldT5Rel7PusxshBp0QibpnZLuLN2elvQZSWdJ2lgqP6r0nNPTL8MHJR1RKp+dyrolLapeo5lZc0XEz4FtPYrnAMvS/WXAsaXyS6KwGhgraQJwBLAqIrZFxHZgFTA7Lds7IlZHRACXlLZlZiPc6ME+MSIeBGYASBoFbASuBj4GnBcRXy2vL2kacALwLuAtwE8lvSMt/jZwGLABuE3Sioi4b7CxmZk1UEdEbEr3NwMd6f5E4LHSehtSWV/lG6qUv46kBRS9bnR0dNDV1VVboGNg4fSdNa1b6zbrsWPHjpbUU4t2igXaK552igXaK55WxjLohKyHQ4GHIuLRPqZEzAGWR8TzwCOSuim68wG6I+JhAEnL07pOyMysrURESIoW1LMEWAIwc+bM6OzsrOl537z0Gs69p7Zmff1JtW2zHl1dXdQae7O1UyzQXvG0UyzQXvG0MpZGJWQnAJeXHp8maS5wO7AwddtPBFaX1in/Ouz5a3JWtUqG4q/GnJn+SKx7JO7zSK67RbZImhARm9Kw49ZUvhGYXFpvUirbCHT2KO9K5ZOqrG9mVn9CJmlX4Bjg9FR0AXA2EOnvucDJ9dYDQ/NXY85MfyTWPRL3eSTX3SIrgHnA4vT3mlL5aalXfxbwVErabgD+vjSR/3Dg9IjYlubaHgzcAswFvtnKHTGz9tWIHrIjgV9GxBaAyl8ASRcC16aHvf2apI9yM7OWkXQ5Re/WeEkbKI6WXAxcIekU4FHg+LT6SuAooBt4jmL+LCnxOhu4La33pYioHCjwKYojOccA16ebmVlDErITKQ1XVrr208OPAJXz+awALpP0NYpJ/VOBWwEBUyXtR5GInQD8WQPiMjMbkIg4sZdFh1ZZN4BTe9nOUmBplfLbgf3ridHMhqe6EjJJe1AcHfnJUvE/SJpBMWS5vrIsIu6VdAXFZP2dwKkR8VLazmnADcAoYGlE3FtPXGZmZmZDSV0JWUQ8C7ypR9lH+1j/HOCcKuUrKbr/zczMzEYcn6nfzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzJyQmZmZmWXmhMzMzMwsMydkZmZmZpk5ITMzMzPLzAmZmZmZWWZOyMzMzMwyqzshk7Re0j2S7pR0eyrbR9IqSevS33GpXJLOl9Qt6W5JB5S2My+tv07SvHrjMjMzMxsqGtVD9scRMSMiZqbHi4CbImIqcFN6DHAkMDXdFgAXQJHAAWcCs4CDgDMrSZyZmZnZcNesIcs5wLJ0fxlwbKn8kiisBsZKmgAcAayKiG0RsR1YBcxuUmxmZmZmbWV0A7YRwI2SAvheRCwBOiJiU1q+GehI9ycCj5WeuyGV9Vb+GpIWUPSs0dHRQVdXV00BdoyBhdN31rRurdus1Y4dOxq+TdfdfvW67jx1m5kNF41IyD4QERslvRlYJemB8sKIiJSs1S0le0sAZs6cGZ2dnTU975uXXsO599S2q+tPqm2bterq6qLWOBttJNY9Evd5JNdtZjZc1D1kGREb09+twNUUc8C2pKFI0t+tafWNwOTS0yelst7KzczMzIa9uhIySXtI2qtyHzgcWAusACpHSs4Drkn3VwBz09GWBwNPpaHNG4DDJY1Lk/kPT2VmZmZmw169Q5YdwNWSKtu6LCJ+Iuk24ApJpwCPAsen9VcCRwHdwHPAxwAiYpuks4Hb0npfiohtdcZmZmZmNiTUlZBFxMPAe6qUPwEcWqU8gFN72dZSYGk98ZiZmZkNRT5Tv5mZmVlmTsjMzMzMMnNCZmZWA18mzsyayQmZmVntfJk4M2uKRpwYdliZsui6mtddv/joJkZiZkPAHKAz3V8GdAGfpXSZOGC1pMpl4jpJl4kDkFS5TNzlrQ3bzNqNEzIzs9r4MnED1E6X1WqnWKC94mmnWKC94mllLE7IzMxq48vEDVA7XVarnWKB9oqnnWKB9oqnlbF4DpmZWQ18mTgzayYnZGZm/fBl4sys2TxkaWbWP18mzsyaygmZmVk/fJk4M2s2D1mamZmZZeaEzMzMzCwzJ2RmZmZmmQ06IZM0WdLNku6TdK+kT6fysyRtTNd7u1PSUaXnnJ6u7fagpCNK5bNTWbekRdXqMzMzMxuu6pnUvxNYGBG/TIeDr0mXAQE4LyK+Wl5Z0jTgBOBdwFuAn0p6R1r8beAwirNW3yZpRUTcV0dsZmZmZkPGoBOydE6dTen+M5Lup8olQErmAMsj4nngEUndFCdWBOhORzEhaXla1wmZmZmZjQgNOe2FpCnAe4FbgPcDp0maC9xO0Yu2nSJZW116Wvkabj2v7Tarl3qafn23gail/pzX5BqJdY/EfR7JdZuZDRd1J2SS9gR+DHwmIp6WdAFwNsWFeM8GzgVOrrceaM313QailmvB5bwm10iseyTu80iu28xsuKgrS5G0C0UydmlEXAUQEVtKyy8Erk0P+7qGm6/tZmZmZiNWPUdZCrgIuD8ivlYqn1Ba7SMU13uD4tpuJ0jaTdJ+wFTgVopLiEyVtJ+kXSkm/q8YbFxmZmZmQ009PWTvBz4K3CPpzlR2BnCipBkUQ5brgU8CRMS9kq6gmKy/Ezg1Il4CkHQaxQV2RwFLI+LeOuIyMzMzG1LqOcryF4CqLFrZx3POAc6pUr6yr+eZmZmZDWc+U7+ZmZlZZk7IzMzMzDJzQmZmZmaWmRMyMzMzs8yckJmZmZll5oTMzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpaZEzIzMzOzzAZ9cXGDKYuu63edhdN3Mn/RdaxffHQLIjIzM7OhyD1kZmZmZpm1TUImabakByV1S1qUOx4zs2Zxe2dmPbVFQiZpFPBt4EhgGnCipGl5ozIzazy3d2ZWTbvMITsI6I6IhwEkLQfmAPdljaqBaplvNhiem2Y25Az79s7MBq5dErKJwGOlxxuAWT1XkrQAWJAe7pD0YI3bHw88XleEg/SXTa5bX+lzcbb9zlj3SNznoVz3WxsZyBDRb3vXiraun7ajUXK+L3tqp1igveJpp1igveJpZCx9tnftkpDVJCKWAEsG+jxJt0fEzCaE5LrbrO6RuM8jue7haii2ddW0UzztFAu0VzztFAu0VzytjKUt5pABG4HJpceTUpmZ2XDj9s7MXqddErLbgKmS9pO0K3ACsCJzTGZmzeD2zsxepy2GLCNip6TTgBuAUcDSiLi3gVUMuOvfdQ/ZukfiPo/kuoecJrd37fa/aKd42ikWaK942ikWaK94WhaLIqJVdZmZmZlZFe0yZGlmZmY2YjkhMzMzM8tsWCdkzbo8iaT1ku6RdKek21PZPpJWSVqX/o5L5ZJ0forhbkkHlLYzL62/TtK8XupaKmmrpLWlsobVJenAtC/d6bnqp+6zJG1M+36npKNKy05P23lQ0hGl8qr/hzSp+ZZU/qM0wbmybLKkmyXdJ+leSZ9uxb73UW/T91vSGyTdKumuVPcX+1l/t/S4Oy2fUkdMvdV9saRHSvs9oxnvNatfb//bJtbXsM9oA2MaJekOSdemxwP+7DQwlrGSrpT0gKT7Jb0v12sj6a/S/2itpMvT571lr42a/D3WoHj+Mf2v7pZ0taSxpWUDak8HLSKG5Y1isuxDwNuAXYG7gGkN2vZ6YHyPsn8AFqX7i4CvpPtHAdcDAg4Gbknl+wAPp7/j0v1xVer6IHAAsLYZdQG3pnWVnntkP3WfBfxNlTinpdd4N2C/9NqP6uv/AFwBnJDufxf436XtTQAOSPf3An6V6mjqvvdRb9P3O8WxZ7q/C3BLiq+39T8FfDfdPwH4UR0x9Vb3xcBxVfa7oe8139q3veujzoZ8Rhsc018DlwHXpscD+uw0OJZlwMfT/V2BsTleG4oTET8CjCm9JvNb+drQ5O+xBsVzODA63f9KKZ4Bt6eDvQ3nHrJXLk8SES8AlcuTNMscig8g6e+xpfJLorAaGCtpAnAEsCoitkXEdmAVMLvnRiPi58C2ZtSVlu0dEaujeOddUtpWb3X3tf/LI+L5iHgE6Kb4H1T9P6TekUOAK6vsBxGxKSJ+me4/A9xP0bA0dd/7qLfp+51i35HKd0m36ON1Kr8WVwKHpu0PKKZ+6u5rvxv2XrO6tbq9a+RntCEkTQKOBr6fHvfVxvT22WlULG+k+NK/CCAiXoiIJ8n02lCcUWGMpNHA7sAmWvjaNPN7rFHxRMSNEbEzPVxNcX7ASjwDak8HazgnZNUuT9LXF+tABHCjpDUqLnEC0BERm9L9zUBHP3HUE1+j6pqY7g80htNSt+7SSjfzIOp+E/Bk6QPQa92py/y9FL02Ldv3HvW2ZL/TkMudwFaKBuehPtZ/pY60/Km0/UG953rWHRGV/T4n7fd5knYb5H4P9r1mtWlme9evOj+jjfJ14O+Al9Pjvj5rvX12GmU/4DfAD9IQ6vcl7UGG1yYiNgJfBX5NkYg9Bawh32tT0crvzIE6maKXrqXxDOeErJk+EBEHUAxxnSrpg+WFqQegJecTaWVdyQXA7wMzKD7c5zazMkl7Aj8GPhMRT5eXNXPfq9Tbkv2OiJciYgbFr7ODgD9oRj211C1pf+D0FMMfUQwVfLZV8djQkOsz2iOGDwNbI2JNs+uq0WiKIbELIuK9wLMUw3KvaOFrM46i52Y/4C3AHgyyZ6lZMnyP9UrS54CdwKWtrns4J2RNuzxJ+sVBRGwFrqb44txS6WJOf7f2E0c98TWqro282i1bUwwRsSV9cb8MXEix74Op+wmKrujRPcpfIWkXiob+0oi4qlX7Xq3eVu53qu9J4GbgfX2s/0odafkb0/bres+V6p6dhqUiIp4HflDHfg/4vWYDkuVyTA36jDbC+4FjJK2nGDo6BPgGA//sNMoGYEOpl/lKigQtx2vzIeCRiPhNRLwIXEXxeuV6bSpa+Z1ZE0nzgQ8DJ6UksaXxDOeErCmXJ5G0h6S9KvcpJgKuTduel1abB1yT7q8A5qYjRw4GnkrdtDcAh0sal37BHJ7KatGQutKypyUdnOYIzC1tq7f9L89r+Eja90rdJ6g4Qmc/YCrFJO6q/4f0Zr8ZOK7KflTmf1wE3B8RX2vVvvdWbyv2W9K+Skf2SBoDHEYxL6e316n8WhwH/Cxtf0Ax9VH3A6UGUxRzPMr73dT3mg1Iyy/H1MDPaN0i4vSImBQRUyj2/WcRcRID/+w0RERsBh6T9M5UdChwHxleG4qhyoMl7Z7+Z5VYsrw2Ja38zuyXpNkUQ97HRMRzPeIcUHs6aNHgI0va6UZxtMavKObhfK5B23wbxdEUdwH3VrZLMcZ+E7AO+CmwTyoX8O0Uwz3AzNK2TqaYINgNfKyX+i6nGCJ7keJX1ymNrAuYSfEl+xDwLdLVG/qo+4dp23enN9+E0vqfS9t5kNcerVn1/5Bey1tTTP8H2K207AMUXdh3A3em21HN3vc+6m36fgPvBu5IdawFvtDP+m9Ij7vT8rfVEVNvdf8s7fda4J949UjMhr7XfGvP9q6f+hr2GW1wXJ28epTlgD87DYxjBnB7en3+meLIwCyvDfBF4IH0+fshxRGDLXttaPL3WIPi6aaYE1Z5L3+3tP6A2tPB3nzpJDMzM7PMhvOQpZmZmdmQ4ITMzMzMLDMnZGZmZmaZOSEzMzMzy8wJmZmZmVlmTsjMzMzMMnNCZmZmZpbZ/w+5SCfmrIFAEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "X.drop([\"Latitude\", \"Longitude\"], axis=1, inplace=True)\n",
    "\n",
    "X.hist(bins=20, figsize=(10, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model score: 0.490618615960575 +- 0.03617289854929812\n"
     ]
    }
   ],
   "source": [
    "linmod = Ridge(random_state=10)\n",
    "\n",
    "cv = cross_validate(linmod, X, y)\n",
    "\n",
    "mean_, std_ = np.mean(cv[\"test_score\"]), np.std(cv[\"test_score\"])\n",
    "\n",
    "print(f\"Model score: {mean_} +- {std_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model score: 0.5553526813919298 +- 0.0224451399278526\n"
     ]
    }
   ],
   "source": [
    "# get the name of the created features\n",
    "\n",
    "spl = SplineTransformer(degree=3, n_knots=50)\n",
    "\n",
    "ct = ColumnTransformer(\n",
    "    [(\"splines\", spl, [\"AveRooms\", \"AveBedrms\", \"Population\", \"AveOccup\"])],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "ct.fit(X, y)\n",
    "\n",
    "cv = cross_validate(linmod, ct.transform(X), y)\n",
    "\n",
    "mean_, std_ = np.mean(cv[\"test_score\"]), np.std(cv[\"test_score\"])\n",
    "\n",
    "print(f\"Model score: {mean_} +- {std_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['splines__AveRooms_sp_0', 'splines__AveRooms_sp_1',\n",
       "       'splines__AveRooms_sp_2', 'splines__AveRooms_sp_3',\n",
       "       'splines__AveRooms_sp_4', 'splines__AveRooms_sp_5',\n",
       "       'splines__AveRooms_sp_6', 'splines__AveRooms_sp_7',\n",
       "       'splines__AveRooms_sp_8', 'splines__AveRooms_sp_9',\n",
       "       'splines__AveRooms_sp_10', 'splines__AveRooms_sp_11',\n",
       "       'splines__AveRooms_sp_12', 'splines__AveRooms_sp_13',\n",
       "       'splines__AveRooms_sp_14', 'splines__AveRooms_sp_15',\n",
       "       'splines__AveRooms_sp_16', 'splines__AveRooms_sp_17',\n",
       "       'splines__AveRooms_sp_18', 'splines__AveRooms_sp_19',\n",
       "       'splines__AveRooms_sp_20', 'splines__AveRooms_sp_21',\n",
       "       'splines__AveRooms_sp_22', 'splines__AveRooms_sp_23',\n",
       "       'splines__AveRooms_sp_24', 'splines__AveRooms_sp_25',\n",
       "       'splines__AveRooms_sp_26', 'splines__AveRooms_sp_27',\n",
       "       'splines__AveRooms_sp_28', 'splines__AveRooms_sp_29',\n",
       "       'splines__AveRooms_sp_30', 'splines__AveRooms_sp_31',\n",
       "       'splines__AveRooms_sp_32', 'splines__AveRooms_sp_33',\n",
       "       'splines__AveRooms_sp_34', 'splines__AveRooms_sp_35',\n",
       "       'splines__AveRooms_sp_36', 'splines__AveRooms_sp_37',\n",
       "       'splines__AveRooms_sp_38', 'splines__AveRooms_sp_39',\n",
       "       'splines__AveRooms_sp_40', 'splines__AveRooms_sp_41',\n",
       "       'splines__AveRooms_sp_42', 'splines__AveRooms_sp_43',\n",
       "       'splines__AveRooms_sp_44', 'splines__AveRooms_sp_45',\n",
       "       'splines__AveRooms_sp_46', 'splines__AveRooms_sp_47',\n",
       "       'splines__AveRooms_sp_48', 'splines__AveRooms_sp_49',\n",
       "       'splines__AveRooms_sp_50', 'splines__AveRooms_sp_51',\n",
       "       'splines__AveBedrms_sp_0', 'splines__AveBedrms_sp_1',\n",
       "       'splines__AveBedrms_sp_2', 'splines__AveBedrms_sp_3',\n",
       "       'splines__AveBedrms_sp_4', 'splines__AveBedrms_sp_5',\n",
       "       'splines__AveBedrms_sp_6', 'splines__AveBedrms_sp_7',\n",
       "       'splines__AveBedrms_sp_8', 'splines__AveBedrms_sp_9',\n",
       "       'splines__AveBedrms_sp_10', 'splines__AveBedrms_sp_11',\n",
       "       'splines__AveBedrms_sp_12', 'splines__AveBedrms_sp_13',\n",
       "       'splines__AveBedrms_sp_14', 'splines__AveBedrms_sp_15',\n",
       "       'splines__AveBedrms_sp_16', 'splines__AveBedrms_sp_17',\n",
       "       'splines__AveBedrms_sp_18', 'splines__AveBedrms_sp_19',\n",
       "       'splines__AveBedrms_sp_20', 'splines__AveBedrms_sp_21',\n",
       "       'splines__AveBedrms_sp_22', 'splines__AveBedrms_sp_23',\n",
       "       'splines__AveBedrms_sp_24', 'splines__AveBedrms_sp_25',\n",
       "       'splines__AveBedrms_sp_26', 'splines__AveBedrms_sp_27',\n",
       "       'splines__AveBedrms_sp_28', 'splines__AveBedrms_sp_29',\n",
       "       'splines__AveBedrms_sp_30', 'splines__AveBedrms_sp_31',\n",
       "       'splines__AveBedrms_sp_32', 'splines__AveBedrms_sp_33',\n",
       "       'splines__AveBedrms_sp_34', 'splines__AveBedrms_sp_35',\n",
       "       'splines__AveBedrms_sp_36', 'splines__AveBedrms_sp_37',\n",
       "       'splines__AveBedrms_sp_38', 'splines__AveBedrms_sp_39',\n",
       "       'splines__AveBedrms_sp_40', 'splines__AveBedrms_sp_41',\n",
       "       'splines__AveBedrms_sp_42', 'splines__AveBedrms_sp_43',\n",
       "       'splines__AveBedrms_sp_44', 'splines__AveBedrms_sp_45',\n",
       "       'splines__AveBedrms_sp_46', 'splines__AveBedrms_sp_47',\n",
       "       'splines__AveBedrms_sp_48', 'splines__AveBedrms_sp_49',\n",
       "       'splines__AveBedrms_sp_50', 'splines__AveBedrms_sp_51',\n",
       "       'splines__Population_sp_0', 'splines__Population_sp_1',\n",
       "       'splines__Population_sp_2', 'splines__Population_sp_3',\n",
       "       'splines__Population_sp_4', 'splines__Population_sp_5',\n",
       "       'splines__Population_sp_6', 'splines__Population_sp_7',\n",
       "       'splines__Population_sp_8', 'splines__Population_sp_9',\n",
       "       'splines__Population_sp_10', 'splines__Population_sp_11',\n",
       "       'splines__Population_sp_12', 'splines__Population_sp_13',\n",
       "       'splines__Population_sp_14', 'splines__Population_sp_15',\n",
       "       'splines__Population_sp_16', 'splines__Population_sp_17',\n",
       "       'splines__Population_sp_18', 'splines__Population_sp_19',\n",
       "       'splines__Population_sp_20', 'splines__Population_sp_21',\n",
       "       'splines__Population_sp_22', 'splines__Population_sp_23',\n",
       "       'splines__Population_sp_24', 'splines__Population_sp_25',\n",
       "       'splines__Population_sp_26', 'splines__Population_sp_27',\n",
       "       'splines__Population_sp_28', 'splines__Population_sp_29',\n",
       "       'splines__Population_sp_30', 'splines__Population_sp_31',\n",
       "       'splines__Population_sp_32', 'splines__Population_sp_33',\n",
       "       'splines__Population_sp_34', 'splines__Population_sp_35',\n",
       "       'splines__Population_sp_36', 'splines__Population_sp_37',\n",
       "       'splines__Population_sp_38', 'splines__Population_sp_39',\n",
       "       'splines__Population_sp_40', 'splines__Population_sp_41',\n",
       "       'splines__Population_sp_42', 'splines__Population_sp_43',\n",
       "       'splines__Population_sp_44', 'splines__Population_sp_45',\n",
       "       'splines__Population_sp_46', 'splines__Population_sp_47',\n",
       "       'splines__Population_sp_48', 'splines__Population_sp_49',\n",
       "       'splines__Population_sp_50', 'splines__Population_sp_51',\n",
       "       'splines__AveOccup_sp_0', 'splines__AveOccup_sp_1',\n",
       "       'splines__AveOccup_sp_2', 'splines__AveOccup_sp_3',\n",
       "       'splines__AveOccup_sp_4', 'splines__AveOccup_sp_5',\n",
       "       'splines__AveOccup_sp_6', 'splines__AveOccup_sp_7',\n",
       "       'splines__AveOccup_sp_8', 'splines__AveOccup_sp_9',\n",
       "       'splines__AveOccup_sp_10', 'splines__AveOccup_sp_11',\n",
       "       'splines__AveOccup_sp_12', 'splines__AveOccup_sp_13',\n",
       "       'splines__AveOccup_sp_14', 'splines__AveOccup_sp_15',\n",
       "       'splines__AveOccup_sp_16', 'splines__AveOccup_sp_17',\n",
       "       'splines__AveOccup_sp_18', 'splines__AveOccup_sp_19',\n",
       "       'splines__AveOccup_sp_20', 'splines__AveOccup_sp_21',\n",
       "       'splines__AveOccup_sp_22', 'splines__AveOccup_sp_23',\n",
       "       'splines__AveOccup_sp_24', 'splines__AveOccup_sp_25',\n",
       "       'splines__AveOccup_sp_26', 'splines__AveOccup_sp_27',\n",
       "       'splines__AveOccup_sp_28', 'splines__AveOccup_sp_29',\n",
       "       'splines__AveOccup_sp_30', 'splines__AveOccup_sp_31',\n",
       "       'splines__AveOccup_sp_32', 'splines__AveOccup_sp_33',\n",
       "       'splines__AveOccup_sp_34', 'splines__AveOccup_sp_35',\n",
       "       'splines__AveOccup_sp_36', 'splines__AveOccup_sp_37',\n",
       "       'splines__AveOccup_sp_38', 'splines__AveOccup_sp_39',\n",
       "       'splines__AveOccup_sp_40', 'splines__AveOccup_sp_41',\n",
       "       'splines__AveOccup_sp_42', 'splines__AveOccup_sp_43',\n",
       "       'splines__AveOccup_sp_44', 'splines__AveOccup_sp_45',\n",
       "       'splines__AveOccup_sp_46', 'splines__AveOccup_sp_47',\n",
       "       'splines__AveOccup_sp_48', 'splines__AveOccup_sp_49',\n",
       "       'splines__AveOccup_sp_50', 'splines__AveOccup_sp_51',\n",
       "       'remainder__MedInc', 'remainder__HouseAge'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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